Bibliographic record
Abstract
The aim of this study is to conduct a bibliometric analysis of articles on evidence-based medicine. Using Bibliometrix and VOSviwer software, the most efficient author, country, organization, and journals were identified. Web of Science articles between the years of 1975-2019 were downloaded with a search strategy and analyzed with Bibliometrix and VOSviwer software. It has been observed that evidence-based medicine articles were grouped under three main clusters (Management and Decision Support, Drug and Experiment and Measurment). The first three countries that have the highest international collaboration rate are Switzerland, New Zealand, and Sweden. The first five countries regarding publication numbers are the USA, United Kingdom, Canada, Australia, and Germany. While Khan and Green have the highest grade in h and g index; Baglı, Castagnetti and Fossum have the highest grade in m index. Guyatt is the author who has the highest number of citations whereas Phillips is the one who has the most publications. While, on one hand, evidence-based medicine extends its function in illness and drug treatments, on the other hand, it is used as policy input to improve the education, curriculum, and the health system. Policy-makers, decision-makers, educators, and researchers can develop strategies according to the findings identified above.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.225 | 0.200 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".